Instructions to use ProbeX/Model-J__ResNet__model_idx_0083 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0083 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0083") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0083") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0083", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0712ab000cdccd9cec2f9d911059a14d5fa9d687ebfde62c64cc6f8d015452fe
- Size of remote file:
- 171 MB
- SHA256:
- 5c396ced22a034425d839f97db4dcd7d261280757e8feda8bca396d8ffe80359
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